The Challenge
Navigating Complexity
Unexpected machinery breakdowns were causing massive production halts, costing millions of dollars annually in repairs and lost time. Maintenance was reactive rather than proactive, meaning parts were replaced only after they failed, causing cascading damage to other components.
Our Solution
Engineered for Scale
We deployed a network of IoT sensors across critical machinery to collect vibration, thermal, and acoustic data. This data was fed into a custom TensorFlow model trained to recognize the micro-signatures of impending component failure. We built a real-time dashboard on Azure that alerts maintenance teams to specific anomalies.
